Comparative Analysis of SDG Implementation Evolution Worldwide

Lodrik Adam, Sofia Benczédi, Stefan Favre, Delia Fuchs

Introduction

  1. What can explain the state of the countries regarding sustainable development?

  2. How are the different SDGs linked?

  3. How has the adoption of the SDGs in 2015 influenced the achievement of SDGs?

  4. Is the evolution in sustainable development influenced by uncontrollable events, such as economic crisis, health crisis and natural disasters?

Main dataset

  1. SDG achievement scores, overall & individual scores (except goal 14: life under water)

Complementary datasets

  1. Unemployment rate

  2. GDP per capita and military expenditure in % of GDP

  3. Internet usage, % of the population using internet

  4. Human freedom index (personal and economic), movement, religion, trade…

  5. Disasters, deaths, injured…

  6. COVID, deaths, cases and stringency

  7. Conflicts, deaths, population and area affected

  • Continents, Regions
  • Match datasets by key = (country, year)

EDA / Analysis

Factors influence over SDG goals

  • distribution results: EU best performance.
  • Africa: lack of resilient/sustainable industries and tech.
  • Americas: lack of reduction of inequalities within/between countries
  • Correlations results: negative correlations between goals12 & 13 and the factors

Factors influence over SDG goals

  • global regression results: Factors influencing positively and negatively the goals (i.e unemployment.rate)
  • Some variables such as internet_usage or ef_legal only influencing positively goals

How are the different SDGs linked?

  • Interconnection of the goals
  • Negative correlation between Responsible Consumption and Production and Climate Action and the other goals

How are the different SDGs linked?

  • Strange association between Reduced Inequalities and Life on Land goals

Evolution over time

Evolution over time

Influence of events over the SDG scores

Disaster variables correlation coefficients suggests a weak insignificant negative relationship.

Covid-19 variables they have a weak positive relationship but are highly significant.

Conflicts variables indicate a strong significant negative correlation with the overall score.

Influence of events over the SDG scores

Correlation between COVID-19 variables and the SDG goals with 1 year gap

Regressions for the Goal 9 with Cases per million variable in the COVID-19 dataset

Conclusion

Global Progress on SDGs:

  • Slow growth of Sustainable Development Goals (SDGs) globally.
  • Africa lags behind other continents in SDG achievement.
  • Europe stands out as a leader in SDG accomplishment.
  • Oceania exhibits greater variations (both positive and negative) in SDG performance.

Interconnectedness of SDGs:

  • High achievement in one SDG often correlates with high scores in other goals, except for goals 12 and 13 (related to climate action) where the correlation appears opposite.

Influential Factors:

  • Higher Internet usage and ef_legal tends to correlate with higher scores in SDGs. Other influencing factors exhibit diverse effects on SDG achievement; for example, unemployment rates impact varies across different goals.
  • Climate disasters display weak or negligible relationships with SDGs. Limited associations exist between climate-related variables and specific goals, with no significant overall impact.
  • Variables related to Covid-19 and conflicts show significance in SDG achievement but explain limited variance for each objective.

SDG Adoption and Partnership:

  • Increased adoption of SDGs encourages greater partnerships across the goals, particularly aligning with Goal 16.
  • Goal 9 (industry, innovation, and infrastructure) shows a faster rate of advancement compared to other goals.

Comparative Analysis of SDG Implementation Evolution Worldwide